We conduct experiments with 20 objects in the YCB Object and Model Set [1]}. During around 12 robot hours, our system collected 497 scenes, 6,541 RGB-D images and 22,851 object instances in these images with accurate 6D poses. In this process, human intervention is required in two cases: 1) whenever a new subset of the 20 YCB objects needs to be presented to the robot, and 2) to rearrange objects if the system cannot initialize the object poses for a while. Compared to human collected videos for pose annotation, our system saves a significant amount of human labor, and has the ability to scale up to a large number of scenes. For comparison, the YCB-Video dataset [2]} and the LabelFusion dataset [3]} have 92 scenes and 138 scenes, respectively. We split the collected data into a training set (265 scenes, 3,590 images) and a test set (232 scenes, 2,951 images).
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